Precision, Recall and Correctly Classified Instances - precision

Question

How to calculate the Precision and Recall for yes, no class:
TP Rate FP Rate Precision Recall F-Measure ROC Area Class
0.444 0.8 0.5 0.444 0.471 0.522 yes
0.2 0.556 0.167 0.2 0.182 0.522 no
Weighted Avg. 0.357 0.713 0.381 0.357 0.367 0.522
and Correctly Classified Instances is 35.714%
Data weather with Weka
#relation weather
#attribute outlook {sunny, overcast, rainy}
#attribute temperature real
#attribute humidity real
#attribute windy {TRUE, FALSE}
#attribute play {yes, no}
No. outlook temperature humidity windy play
1 sunny 85.0 85.0 FALSE no
2 sunny 80.0 90.0 TRUE no
3 overcast 83.0 86.0 FALSE yes
4 rainy 70.0 96.0 FALSE yes
5 rainy 68.0 80.0 FALSE yes
6 rainy 65.0 70.0 TRUE no
7 overcast 64.0 65.0 TRUE yes
8 sunny 72.0 95.0 FALSE no
9 sunny 69.0 70.0 FALSE yes
10 rainy 75.0 80.0 FALSE yes
11 sunny 75.0 70.0 TRUE yes
12 overcast 72.0 90.0 TRUE yes
13 overcast 81.0 75.0 FALSE yes
14 rainy 71.0 91.0 TRUE no
===========================
=== Run information ===
Scheme:weka.classifiers.rules.PART -M 2 -C 0.25 -Q 1
Relation: weather
Instances: 14
Attributes: 5
outlook
temperature
humidity
windy
play
Test mode:10-fold cross-validation
=== Classifier model (full training set) ===
PART decision list
------------------
outlook = overcast: yes (4.0)
windy = TRUE: no (4.0/1.0)
outlook = sunny: no (3.0/1.0)
: yes (3.0)
Number of Rules : 4
Time taken to build model: 0 seconds
=== Stratified cross-validation ===
=== Summary ===
Correctly Classified Instances 5 35.7143 %
Incorrectly Classified Instances 9 64.2857 %
Kappa statistic -0.3404
Mean absolute error 0.5518
Root mean squared error 0.6935
Relative absolute error 115.875 %
Root relative squared error 140.5649 %
Total Number of Instances 14
=== Detailed Accuracy By Class ===
TP Rate FP Rate Precision Recall F-Measure ROC Area Class
0.444 0.8 0.5 0.444 0.471 0.522 yes
0.2 0.556 0.167 0.2 0.182 0.522 no
Weighted Avg. 0.357 0.713 0.381 0.357 0.367 0.522
=== Confusion Matrix ===
a b <-- classified as
4 5 | a = yes
4 1 | b = no
Thanks and best regards

Answer 1

From the confusion matrix:
=== Confusion Matrix ===
a b <-- classified as
4 5 | a = yes
4 1 | b = no
The Precision is computed as 4/8, i.e. the number of correctly classified a (yes) divided by the number of predicted a, while Recall is 4/9, the number of correctly classified a (yes) divided by the total number of true a. The precision and recall for the other class is the converse.
See the definitions of all those criteria in one single cheatsheet.